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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
68
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
117
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

476
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
476
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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马尔科夫模型建设的信息瓶方法.

Dedi Wang1, Yunrui Qiu2,3, Eric R Beyerle4

  • 1Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.

Journal of chemical theory and computation
|June 11, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了状态预测信息瓶 (SPIB),用于从分子动力学模拟中构建马尔科夫状态模型 (MSM). SPIB提供了一种更准确,更易于解释的方法来分析蛋白质动力学和构建多分辨率模型.

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科学领域:

  • 计算化学和生物物理学
  • 机器学习用于分子动力学.
  • 复杂系统的统计力学.

背景情况:

  • 马尔科夫状态模型 (MSM) 对于从模拟中分析蛋白质动态至关重要.
  • 构建MSM需要定义状态,以捕捉缓慢的动态,并在所选的延迟时间内确保内部放松.
  • 现有的方法通常需要手动调整,并且可能优先考虑缓慢的动态,而不是准确的状态识别.

研究的目的:

  • 引入一种新的连续嵌入方法,即状态预测信息瓶 (SPIB),用于构建马尔科夫状态模型.
  • 为了证明SPIB能够同时执行维度缩小和状态空间分区的能力.
  • 为构建多解析度MSM提供自动化和自我一致的方法.

主要方法:

  • 使用机器学习的连续基础集用于分子构造嵌入.
  • 应用国家预测信息瓶 (SPIB) 框架来减少维度和国家分割.
  • 在没有明确的VAMP-score优化的情况下,评估小蛋白系统上的SPIB性能.

主要成果:

  • 在识别缓慢的动态过程和构建预测多解析度MSM时,SPIB实现了最先进的性能.
  • 基于最小时间分辨率,SPIB自主调整元稳定状态的数量,消除了手动干预的需要.
  • 与基于VAMP的方法相比,SPIB准确地区分了元稳定状态并捕获了众多宏观状态,提供了更好的动态路径解释性.

结论:

  • SPIB为端到端的马尔科夫状态模型构建提供了一种有效,自动化和可解释的方法.
  • 持续嵌入的方法提高了对蛋白质结构动态的理解.
  • SPIB代表了分子动力学模拟分析的重大进步.